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Predicting the Material Footprint in Germany between 2015 and 2020 via Seasonally Decomposed Autoregressive and Exponential Smoothing Algorithms

Author

Listed:
  • Johannes Buhl

    (Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany)

  • Christa Liedtke

    (Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany
    Industrial Design, Folkwang University of the Arts, Klemensborn 39, 45239 Essen, Germany)

  • Sebastian Schuster

    (Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany)

  • Katrin Bienge

    (Wuppertal Institute for Climate, Environment and Energy, Doeppersberg 19, 41203 Wuppertal, Germany)

Abstract

Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whether the Material Footprint decreases accordingly. We studied the Material Footprint of 113,559 users of an online footprint calculator and predicted the Material Footprint by seasonally decomposed autoregressive (STL-ARIMA) and exponential smoothing (STL-ETS) algorithms. We find a relatively stable Material Footprint for private consumption. The overall Material Footprint decreased by 0.4% per year between 2015 and 2020 on average. The predictions do not suggest that the Material Footprint of private consumption follows the reduction path of 3.3% per year that will lead to the sustainable consumption of natural resources.

Suggested Citation

  • Johannes Buhl & Christa Liedtke & Sebastian Schuster & Katrin Bienge, 2020. "Predicting the Material Footprint in Germany between 2015 and 2020 via Seasonally Decomposed Autoregressive and Exponential Smoothing Algorithms," Resources, MDPI, vol. 9(11), pages 1-17, October.
    • Handle: RePEc:gam:jresou:v:9:y:2020:i:11:p:125-:d:434483
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    References listed on IDEAS

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